5 Key Considerations When Choosing a Data Science Platform

women thinking about data scienceOrganizations today are gathering mass quantities of data from a multitude of sources in an effort to gain insights that will improve their business outcomes. But what good is a stockpile of data if the information derived is not analyzed and acted upon quickly and correctly? The ability to rapidly uncover patterns in collected data – and to deliver insights at the right point of impact and at the right time – is paramount to operational success.

In order to achieve successful, data-driven business decisions, organizations are investing in data science and advanced analytics capabilities. These are key considerations when selecting a data science platform to ensure you get the most from your investment.

1. Openness
A collaborative, cohesive and interoperable offering of software and hardware for speed to innovation without vendor lock-in is essential for a data science team’s success.

2. Efficiency
In an effort to prevent data center sprawl, storage/cluster siloes and ballooning TCO, a single integrated platform can lead to better results and therefore a greater business value.

3. Flexibility
Flexibility to use either private or public cloud ensures your platform can evolve with the needs and demands of your workload.

4. Performance
Driving faster time-to-insights and performance for deep learning workloads means increased data science team productivity and a reduction in model training time.

5. Support
Access to an industry-leading team of experts for training, installation and support with a single point of contact provides confidence and peace of mind.

By leveraging a flexible and collaborative data science environment, organizations can infuse data-driven decisions into operational and customer-facing systems for improved business outcomes and enhanced customer experience.

Accelerate your database transformation journey with a turnkey solution bundle.

IBM and Hortonworks have come together to deliver a solution with deep integration between products that are built for big data and analytics. IBM Data Science Experience with Hortonworks HDP Platform on IBM Power Systems is the only Hortonworks-certified data science platform that is enterprise-ready, open-source, and collaborative.

Sirius and IBM are working together to help organizations accelerate their business and provide a competitive edge, so they can innovate like never before. Connect with a Sirius expert to get started.

By |2018-12-26T21:41:43+00:00August 6th, 2018|Blog|Comments Off on 5 Key Considerations When Choosing a Data Science Platform

About the Author:

Niel Balsino is a Senior Power Engineer-Linux with Sirius Computer Solutions.